Incucyte Plots - KK

library(ggplot2)
library(data.table)
library(scales)
library(RColorBrewer)
library(ggrepel)

data_dir <- here::here('..','data')
load(file.path(data_dir, 'incucyte.Rdata'))
incucyte_dt[, day_f := factor(day_f, levels=sort(as.numeric(levels(day_f))))]

car_set <- c('41BB','CD28','BAFF-R','CD40','TACI','TNR8','KLRG1','zeta')
chosen_measure <- 'redintensity'

Killing, CD4 and CD8 separately

#4 and 8 separate
ggplot(incucyte_dt[k562=='mkate_cd19' & !noise & t_type == 'cd4' & measurement == chosen_measure & car %in% car_set][, 
    car := factor(car, levels=car_set)], 
  aes(x=Elapsed, y=mean_value_norm, group=interaction(car, day_f,donor), fill=car)) + 
  geom_line(aes(color=car)) + 
  geom_ribbon(aes(ymin=mean_value_norm-std_error, ymax=mean_value_norm+std_error), alpha=0.3) +
  facet_grid(t_type+donor~day_f) + 
  ggtitle("Incucyte K562 Killing by mKate Total Integrated Intensity (normalized to starting)") +
  theme_minimal(base_size=20) + geom_vline(xintercept=0) + geom_hline(yintercept=0) +
  theme(plot.title = element_text(size = 25)) + 
  scale_x_continuous(expand=c(0,0), limits=c(0,150)) +
  scale_y_continuous('mKate Total Integrated Intensity\n(normalized to starting)',
    limits=c(1e-2,1e1), trans=log10_trans()) +
  theme(plot.title = element_text(hjust = 0.5)) + 
  scale_color_brewer(palette='Set1') + 
  scale_fill_brewer(palette='Set1') + 
  theme(panel.spacing = unit(2, "lines"))

## Warning: Transformation introduced infinite values in continuous y-axis

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## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Transformation introduced infinite values in continuous y-axis

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## Warning: Removed 11283 row(s) containing missing values (geom_path).

ggplot(incucyte_dt[k562=='mkate_cd19' & !noise & t_type == 'cd8' & measurement == chosen_measure & car %in% car_set][, 
    car := factor(car, levels=car_set)], 
  aes(x=Elapsed, y=mean_value_norm, group=interaction(car, day_f,donor), fill=car)) + 
  geom_line(aes(color=car)) + 
  geom_ribbon(aes(ymin=mean_value_norm-std_error, ymax=mean_value_norm+std_error), alpha=0.3) +
  facet_grid(t_type+donor~day_f) + 
  ggtitle("Incucyte K562 Killing by mKate Total Integrated Intensity (normalized to starting)") +
  theme_minimal(base_size=20) + geom_vline(xintercept=0) + geom_hline(yintercept=0) +
  theme(plot.title = element_text(size = 25)) + 
  scale_x_continuous(expand=c(0,0), limits=c(0,70)) +
  scale_y_continuous('mKate Total Integrated Intensity\n(normalized to starting)',
    limits=c(1e-2,1e1), trans=log10_trans()) +
  theme(plot.title = element_text(hjust = 0.5)) + 
  scale_color_brewer(palette='Set1') + 
  scale_fill_brewer(palette='Set1') + 
  theme(panel.spacing = unit(2, "lines"))

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Removed 606 row(s) containing missing values (geom_path).

Killing, normalized to no-car controls

ggplot(incucyte_dt[k562=='mkate_cd19' & !noise & t_type == 'cd4' & measurement == chosen_measure], 
  aes(x=Elapsed, y=mean_value_none, group=interaction(car, day_f,donor), fill=car)) + 
  geom_line(aes(color=car)) + 
  facet_grid(t_type+donor~day_f) + 
  ggtitle("Incucyte K562 Killing by mKate Total Integrated Intensity (normalized to starting)") +
  theme_minimal(base_size=20) + geom_vline(xintercept=0) + geom_hline(yintercept=0) +
  theme(plot.title = element_text(size = 25)) + 
  scale_x_continuous(expand=c(0,0), limits=c(0,150)) +
  scale_y_continuous('mKate Total Integrated Intensity\n(normalized to growth without CAR)',
    limits=c(1e-2,1e1), trans=log10_trans()) +
  theme(plot.title = element_text(hjust = 0.5)) + 
  scale_color_brewer(palette='Set1') + 
  scale_fill_brewer(palette='Set1') + 
  theme(panel.spacing = unit(2, "lines"))

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Removed 13509 row(s) containing missing values (geom_path).

ggplot(incucyte_dt[k562=='mkate_cd19' & !noise & t_type == 'cd8' & measurement == chosen_measure], 
  aes(x=Elapsed, y=mean_value_none, group=interaction(car, day_f,donor), fill=car)) + 
  geom_line(aes(color=car)) + 
  facet_grid(t_type+donor~day_f) + 
  ggtitle("Incucyte K562 Killing by mKate Total Integrated Intensity (normalized to starting)") +
  theme_minimal(base_size=20) + geom_vline(xintercept=0) + geom_hline(yintercept=0) +
  theme(plot.title = element_text(size = 25)) + 
  scale_x_continuous(expand=c(0,0), limits=c(0,70)) +
  scale_y_continuous('mKate Total Integrated Intensity\n(normalized to growth without CAR)',
    limits=c(1e-2,1e1), trans=log10_trans()) +
  theme(plot.title = element_text(hjust = 0.5)) + 
  scale_color_brewer(palette='Set1') + 
  scale_fill_brewer(palette='Set1') + 
  theme(panel.spacing = unit(2, "lines"))

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Removed 2698 row(s) containing missing values (geom_path).

Smoothed, not ribbon for banff

# 
# incucyte_dt[, value_norm := value/value[Elapsed==0], by=c('well','day','donor','car','measurement','k562','image')]
# incucyte_dt[, mean_value_norm := mean(value_norm), by=c('day','donor','car','Elapsed','measurement','k562')]
# incucyte_dt[, std_dev := sd(value_norm), by=c('day','donor','car','Elapsed','measurement','k562')]
# incucyte_dt[, std_error := std_dev/sqrt(.N), by=c('day','donor','car','Elapsed','measurement','k562')]

car_set <- c('41BB','CD28','BAFF-R','KLRG1','TACI', 'none')
colors <- c(brewer.pal(9,'Blues')[c(6,8)], brewer.pal(9,'Greens')[c(5,7,9)], 'grey30')

incucyte_dt_subset <- incucyte_dt[
  k562=='mkate_cd19' & !noise & t_type == 'cd4' & 
  measurement == chosen_measure & car %in% car_set 
  & donor < 3 ][, 
    car := factor(
      car, 
      levels=c('CD28', '41BB', 'BAFF-R', 'KLRG1', 'TACI', 'none'), 
      labels=c('CD28', '41BB', 'Receptor 1', 'Receptor 3', 'Receptor 4', 'No T cells')
    )]

cd4_banff <- ggplot(incucyte_dt_subset, 
       aes(x=Elapsed, y=mean_value_norm, group=interaction(car, day_f,donor), fill=car)) + 
    geom_smooth(aes(color=car), method='loess', se=T) + 
    facet_grid(t_type+donor~day_f) +
    theme_minimal(base_size=18) + geom_vline(xintercept=0) + geom_hline(yintercept=0) +
    theme(plot.title = element_text(size = 25)) + 
    scale_x_continuous(expand=c(0,0), limits=c(0,150), breaks=c(0, 40, 80, 120)) +
    scale_y_continuous('mKate Total Integrated Intensity\n(normalized to starting)',
                       limits=c(1e-2,1e1), trans=log10_trans(), labels = scales::percent_format(accuracy = 1)) +
    theme(plot.title = element_text(hjust = 0.5)) + 
    scale_color_manual(values=colors) + 
    scale_fill_manual(values=colors) + 
    theme(panel.spacing = unit(0.5, "lines"))

incucyte_dt_subset <- incucyte_dt[
  k562=='mkate_cd19' & !noise & t_type == 'cd8' & 
  measurement == chosen_measure & car %in% car_set 
  & donor < 3 ][, 
    car := factor(
      car, 
      levels=c('CD28', '41BB', 'BAFF-R', 'KLRG1', 'TACI', 'none'), 
      labels=c('CD28', '41BB', 'Receptor 1', 'Receptor 3', 'Receptor 4', 'No T cells')
    )]

cd8_banff <- ggplot(incucyte_dt_subset, 
       aes(x=Elapsed, y=mean_value_norm, group=interaction(car, day_f,donor), fill=car)) + 
    geom_smooth(aes(color=car), method='loess', se=T) + 
    facet_grid(t_type+donor~day_f) +
    theme_minimal(base_size=18) + geom_vline(xintercept=0) + geom_hline(yintercept=0) +
    theme(plot.title = element_text(size = 25)) + 
    scale_x_continuous(expand=c(0,0), limits=c(0,65), breaks=c(0, 15, 30, 45)) +
    scale_y_continuous('mKate Total Integrated Intensity\n(normalized to starting)',
                       limits=c(1e-2,1e1), trans=log10_trans(), labels = scales::percent_format(accuracy = 1)) +
    theme(plot.title = element_text(hjust = 0.5)) + 
    scale_color_manual(values=colors) + 
    scale_fill_manual(values=colors) + 
    theme(panel.spacing = unit(0.5, "lines"))

library(cowplot)

## 
## ********************************************************

## Note: As of version 1.0.0, cowplot does not change the

##   default ggplot2 theme anymore. To recover the previous

##   behavior, execute:
##   theme_set(theme_cowplot())

## ********************************************************

plot_grid(cd4_banff, cd8_banff, ncol=1)

## Warning: Transformation introduced infinite values in continuous y-axis

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6570 rows containing non-finite values (stat_smooth).

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## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 1521 rows containing non-finite values (stat_smooth).

% killing plots

bb28z_dt <- incucyte_dt[car %in% c('41BB','CD28','zeta') & k562 != 'none' & 
                          noise==F]
setnames(bb28z_dt, "car", "ctrl_car")

# function to get elapsed time at which red measurement crosses threshold and 
# get some plots out

k562_thresh <- function(df, t_cell, pct_rem, row_limit, x_limit){
  
  cross_dt <- df[t_type==t_cell & Elapsed<=150, list(
      t_cross = min(Elapsed[order(abs(mean_value_none-(pct_rem/100)))][1:row_limit]),
      cross_thresh = min(mean_value_none)<(pct_rem/100),
      last_mean_val_none = dplyr::last(mean_value_none)),
    by=c('car','k562','donor','measurement','day','t_type','noise')][noise==F]
  cross_dt[, day_f := factor(cross_dt$day, levels=c('0','8','15','22','29'))]

  no_cross_dt <- cross_dt[cross_thresh==F][, t_cross := NA]
   
  cross_dt <- cross_dt[cross_thresh==T]
  
  thresh_plot <- ggplot(df[t_type==t_cell & measurement==chosen_measure & 
                             noise==F & k562=='mkate_cd19' & car!='none']) +
    geom_line(aes(x=Elapsed, y=mean_value_none, 
                  group=interaction(well,day,donor), 
                  color=as.factor(donor))) +
    geom_vline(data=cross_dt[measurement==chosen_measure & k562=='mkate_cd19' & 
                               t_type==t_cell & car!='none'], 
               aes(xintercept=t_cross)) +
    facet_grid(car~day_f+donor)  +
    geom_line(data=bb28z_dt[t_type==t_cell & measurement==chosen_measure & 
                            k562=='mkate_cd19'], 
              aes(x=Elapsed, y=mean_value_none, 
                  group=interaction(well,day,donor,ctrl_car), 
                  linetype=ctrl_car), 
              color='gray') +
    geom_text(data=cross_dt[t_type==t_cell & measurement==chosen_measure & 
                              k562=='mkate_cd19' & car!='none'], 
              aes(x=t_cross-8, y=1.5, label=t_cross)) +
    scale_x_continuous(expand=c(0,0), limits=c(0, x_limit)) +
    scale_y_continuous('mKate Total Integrated Intensity\n(normalized to growth without CAR)',
                       limits=c(1e-2,1e1), trans=log10_trans()) +
    theme_minimal(base_size=20) + 
    geom_vline(xintercept=0) + 
    geom_hline(yintercept=0) +
    theme(plot.title = element_text(size = 20, hjust = 0.5))+
    labs(title=paste0(t_cell, " - Time When ", chosen_measure, " Reaches ", pct_rem, 
                      "% of Starting"),
         fill='Donor',
         color='Donor',
         linetype='Controls')
  
  new_list <- list(cross_dt, no_cross_dt, thresh_plot)
  return(new_list)
}

cd4_60 <- k562_thresh(incucyte_dt, 'cd4', 60, 150, 150)

cd4_60[[1]]

cd4_60[[2]]

cd4_60[[3]]

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## Warning: Removed 4986 row(s) containing missing values (geom_path).

## Warning: Removed 32 rows containing missing values (geom_text).

cd8_60 <- k562_thresh(incucyte_dt, 'cd8', 60, 75, 80)

cd8_60[[1]]

cd8_60[[2]]

cd8_60[[3]]

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## Warning: Removed 908 row(s) containing missing values (geom_path).

## Warning: Removed 28 rows containing missing values (geom_text).

# barplots showing time when 60% red intensity is reached

# 2020.05.04 - KK - still some issues with this... for some CARs, the code is
# not taking the first time at which the threshold is crossed but times that 
# have lowest abs value. need to fix.

ggplot(cd4_60[[1]][measurement==chosen_measure & k562=='mkate_cd19'], 
       aes(x=car, y=t_cross, fill=car)) +
  geom_bar(stat="identity", position=position_dodge()) +
  geom_text(aes(x=car, y=t_cross+5, group=t_cross, label=t_cross), 
            position = position_dodge(width=0.9),
            size=4) +
  geom_text(data=cd4_60[[2]][measurement==chosen_measure & k562=='mkate_cd19'], 
            aes(x=car, y=5, group=t_cross, label='NA'),
            position=position_dodge(width=0.9),
            size=3) +
  facet_grid(donor~day_f,
             labeller=label_both) +
  theme_minimal(base_size=20) + 
  geom_hline(yintercept=-Inf) + 
  geom_vline(xintercept=-Inf) + 
  scale_y_continuous(expand=c(0,0), limits=c(0,65)) +
  scale_fill_brewer(palette='Paired') +
  theme(plot.title=element_text(size=20, hjust=0.5),
        axis.text.x = element_text(angle=90, hjust=1, vjust=0.5),
        panel.spacing=unit(2, "lines")) +
  labs(title='CD4 - Time When Red Intensity Reaches 60% of Starting',
       x='Costim',
       y='Hours When 60% Red Intensity Remains',
       fill='Costim')

ggplot(cd8_60[[1]][measurement==chosen_measure & k562=='mkate_cd19'], 
       aes(x=car, y=t_cross, fill=car)) +
  geom_bar(stat="identity", position=position_dodge()) +
  geom_text(aes(x=car, y=t_cross+2, group=t_cross, label=t_cross), 
            position = position_dodge(width=0.9),
            size=4) +
  geom_text(data=cd8_60[[2]][measurement==chosen_measure & k562=='mkate_cd19'], 
            aes(x=car, y=2, group=t_cross, label='NA'),
            position=position_dodge(width=0.9),
            size=3) +
  facet_grid(donor~day_f,
             labeller=label_both) +
  theme_minimal(base_size=20) + 
  geom_hline(yintercept=-Inf) + 
  geom_vline(xintercept=-Inf) + 
  scale_y_continuous(expand=c(0,0), limits=c(0,50)) +
  scale_fill_brewer(palette='Paired') +
  theme(plot.title=element_text(size=20, hjust=0.5),
        axis.text.x = element_text(angle=90, hjust=1, vjust=0.5),
        panel.spacing=unit(2, "lines")) +
  labs(title='CD8 - Time When Red Intensity Reaches 60% of Starting',
       x='Costim',
       y='Hours When 60% Red Intensity Remains',
       fill='Costim')

# line plots with elapsed time when threshold is reached.

# again, same note as above chunk -- need to be fixed
# need to add ggrepel to these

ggplot(incucyte_dt[k562=='mkate_cd19' & !noise & t_type == 'cd4' & measurement == chosen_measure & car!='none'], 
  aes(x=Elapsed, y=mean_value_none, group=interaction(car, day_f,donor), fill=car)) + 
  geom_line(aes(color=car)) + 
  geom_text(data=cd4_60[[1]][k562=='mkate_cd19' & 
                            measurement==chosen_measure & car!='none'], 
            aes(label=t_cross, colour=car, x=140, y=last_mean_val_none)) +
  facet_grid(t_type+donor~day_f) + 
  ggtitle("Incucyte K562 Killing by mKate Total Integrated Intensity (normalized to starting)") +
  theme_minimal(base_size=20) + 
  geom_vline(xintercept=0) + 
  geom_hline(yintercept=0) +
  theme(plot.title = element_text(size = 25)) + 
  scale_x_continuous(expand=c(0,0), limits=c(0,150)) +
  scale_y_continuous('mKate Total Integrated Intensity\n(normalized to growth without CAR)',
    limits=c(1e-2,1e1), trans=log10_trans()) +
  theme(plot.title = element_text(hjust = 0.5)) + 
  scale_color_brewer(palette='Paired') + 
  scale_fill_brewer(palette='Paired') + 
  geom_line(data=incucyte_dt[k562=='mkate_cd19' & !noise & t_type=='cd4' & measurement==chosen_measure & car=='none'], aes(x=Elapsed, y=mean_value_none),
            color='gray') +
  theme(panel.spacing = unit(2, "lines"))

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Removed 11601 row(s) containing missing values (geom_path).

## Warning: Removed 4 rows containing missing values (geom_text).

## Warning: Removed 1908 row(s) containing missing values (geom_path).

ggplot(incucyte_dt[k562=='mkate_cd19' & !noise & t_type == 'cd8' & measurement == chosen_measure], 
  aes(x=Elapsed, y=mean_value_none, group=interaction(car, day_f,donor), fill=car)) + 
  geom_line(aes(color=car)) + 
  facet_grid(t_type+donor~day_f) + 
  ggtitle("Incucyte K562 Killing by mKate Total Integrated Intensity (normalized to starting)") +
  theme_minimal(base_size=20) + 
  geom_vline(xintercept=0) + 
  geom_hline(yintercept=0) +
  theme(plot.title = element_text(size = 25)) + 
  scale_x_continuous(expand=c(0,0), limits=c(0,70)) +
  scale_y_continuous('mKate Total Integrated Intensity\n(normalized to growth without CAR)',
    limits=c(1e-2,1e1), trans=log10_trans()) +
  theme(plot.title = element_text(hjust = 0.5)) + 
  scale_color_brewer(palette='Set1') + 
  scale_fill_brewer(palette='Set1') + 
  theme(panel.spacing = unit(2, "lines"))

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Removed 2698 row(s) containing missing values (geom_path).

# facet on car and color by day

my_colors <- brewer.pal(n=9, "PuBuGn")[5:9]


ggplot(incucyte_dt[k562=='mkate_cd19' & !noise & t_type == 'cd4' & measurement == chosen_measure & car!='none'], 
  aes(x=Elapsed, y=mean_value_none, group=interaction(car, day_f,donor), fill=day_f)) + 
  geom_line(aes(color=day_f)) + 
  facet_grid(t_type+donor~car) + 
  ggtitle("CD4 - Incucyte K562 Killing by mKate Total Integrated Intensity (normalized to starting)") +
  theme_minimal(base_size=20) + geom_vline(xintercept=0) + geom_hline(yintercept=0) +
  theme(plot.title = element_text(size = 25)) + 
  scale_x_continuous(expand=c(0,0), limits=c(0,150)) +
  scale_y_continuous('mKate Total Integrated Intensity\n(normalized to growth without CAR)',
    limits=c(1e-2,1e1), trans=log10_trans()) +
  theme(plot.title = element_text(hjust = 0.5)) + 
  scale_color_manual(values=my_colors) + 
  scale_fill_manual(values=my_colors) + 
  geom_hline(yintercept=1,
            color='gray60') +
  theme(panel.spacing = unit(2, "lines"))

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Removed 11601 row(s) containing missing values (geom_path).

ggplot(incucyte_dt[k562=='mkate_cd19' & !noise & t_type == 'cd8' & measurement == chosen_measure & car!='none'], 
  aes(x=Elapsed, y=mean_value_none, group=interaction(car, day_f,donor), fill=day_f)) + 
  geom_line(aes(color=day_f)) + 
  facet_grid(t_type+donor~car) + 
  ggtitle("CD8 - Incucyte K562 Killing by mKate Total Integrated Intensity (normalized to starting)") +
  theme_minimal(base_size=20) + geom_vline(xintercept=0) + geom_hline(yintercept=0) +
  theme(plot.title = element_text(size = 25)) + 
  scale_x_continuous(expand=c(0,0), limits=c(0,70)) +
  scale_y_continuous('mKate Total Integrated Intensity\n(normalized to growth without CAR)',
    limits=c(1e-2,1e1), trans=log10_trans()) +
  theme(plot.title = element_text(hjust = 0.5)) + 
  scale_color_manual(values=my_colors) + 
  scale_fill_manual(values=my_colors) + 
  geom_hline(yintercept=1,
            color='gray60') +
  theme(panel.spacing = unit(2, "lines"))

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Removed 2608 row(s) containing missing values (geom_path).

# % cancer cells killed at different timepoints - theo's suggestion at meeting

set_time <- function(df, elapsed_hr){
  cd4_plot <- ggplot(incucyte_dt[k562=='mkate_cd19' & !noise & t_type == 'cd4' &
                                   measurement == chosen_measure & car!='none' &
                                   Elapsed==elapsed_hr], 
    aes(x=car, y=mean_value_none, group=interaction(car, day_f, donor), 
        fill=car, color=car)) + 
    geom_point(aes(color=car)) + 
    geom_linerange(aes(ymin=mean_value_none-std_dev_value_none_log, 
                      ymax=mean_value_none+std_dev_value_none_log)) +
    facet_grid(t_type+donor ~ day_f) + 
    ggtitle(paste0("CD4 - Incucyte K562 Killing by mKate Total Integrated Intensity\n(normalized to starting) at ", elapsed_hr ," Hours")) +
    theme_minimal(base_size=20) +
    theme(plot.title = element_text(size = 25)) + 
    scale_y_continuous('mKate Total Integrated Intensity\n(normalized to growth without CAR)',
      limits=c(1e-2,1e1), trans=log10_trans()) +
    theme(plot.title = element_text(hjust = 0.5)) + 
    scale_color_brewer(palette='Paired') + 
    scale_fill_brewer(palette='Paired') + 
    geom_hline(yintercept=1,
              color='gray60') +
    theme(panel.spacing = unit(2, "lines"),
          axis.text.x = element_text(angle = 90, vjust=0.5))
  
  cd8_plot <- ggplot(incucyte_dt[k562=='mkate_cd19' & !noise & t_type == 'cd8' &
                                   measurement == chosen_measure & car!='none' &
                                   Elapsed==elapsed_hr], 
    aes(x=car, y=mean_value_none, group=interaction(car, day_f, donor), 
        fill=car, color=car)) + 
    geom_point(aes(color=car)) + 
    geom_linerange(aes(ymin=mean_value_none-std_dev_value_none_log, 
                      ymax=mean_value_none+std_dev_value_none_log)) +
    facet_grid(t_type+donor ~ day_f) + 
    ggtitle(paste0("CD8 - Incucyte K562 Killing by mKate Total Integrated Intensity\n(normalized to starting) at ", elapsed_hr ," Hours")) +
    theme_minimal(base_size=20) +
    theme(plot.title = element_text(size = 25)) + 
    scale_y_continuous('mKate Total Integrated Intensity\n(normalized to growth without CAR)',
      limits=c(1e-2,1e1), trans=log10_trans()) +
    theme(plot.title = element_text(hjust = 0.5)) + 
    scale_color_brewer(palette='Paired') + 
    scale_fill_brewer(palette='Paired') + 
    geom_hline(yintercept=1,
              color='gray60') +
    theme(panel.spacing = unit(2, "lines"),
          axis.text.x = element_text(angle = 90, vjust=0.5))
  
  new_list <- list(cd4_plot, cd8_plot)
  return(new_list)
}


set_time(incucyte_dt, 26)[[1]]

## Warning in self$trans$transform(x): NaNs produced

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Removed 24 rows containing missing values (geom_segment).

## Warning: Removed 9 rows containing missing values (geom_segment).

set_time(incucyte_dt, 26)[[2]]

## Warning in self$trans$transform(x): NaNs produced

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Removed 4 rows containing missing values (geom_segment).

## Warning: Removed 36 rows containing missing values (geom_segment).

## Warning: Removed 24 rows containing missing values (geom_segment).

## Warning: Removed 8 rows containing missing values (geom_segment).

## Warning: Removed 36 rows containing missing values (geom_segment).

## Warning: Removed 36 rows containing missing values (geom_segment).

## Warning: Removed 18 rows containing missing values (geom_segment).

## Warning: Removed 30 rows containing missing values (geom_segment).

Dan’s single-plot version

# plot_set_time <- function(df, elapsed_hr){
#   
#   t_type <- df[, unique(t_type)]
#   stopifnot(length(t_type) == 1)
#   meausrement <- 
#   
#   plot <- ggplot(df[Elapsed==elapsed_hr], 
#     aes(x=car, y=mean_value_none, group=interaction(car, day_f, donor), 
#         fill=car, color=car)) + 
#     geom_point(aes(color=car)) + 
#     geom_linerange(aes(ymin=mean_value_none-std_dev_value_none_log, 
#                       ymax=mean_value_none+std_dev_value_none_log)) +
#     facet_grid(donor ~ day_f) + 
#     ggtitle(paste(t_type," - Incucyte K562 Killing by mKate Total Integrated Intensity\n(normalized to starting) at ", elapsed_hr ," Hours")) +
#     theme_minimal(base_size=20) +
#     theme(plot.title = element_text(size = 25)) + 
#     scale_y_continuous('mKate Total Integrated Intensity\n(normalized to growth without CAR)',
#       limits=c(1e-2,1e1), trans=log10_trans()) +
#     theme(plot.title = element_text(hjust = 0.5)) + 
#     scale_color_brewer(palette='Paired') + 
#     scale_fill_brewer(palette='Paired') + 
#     geom_hline(yintercept=1,
#               color='gray60') +
#     theme(panel.spacing = unit(2, "lines"),
#           axis.text.x = element_text(angle = 90, vjust=0.5))
# }
# 
# plot_set_time(incucyte_df[
#   k562=='mkate_cd19' & !noise & t_type == 'cd4' &
#   measurement == chosen_measure & car!='none']) +
#   labs(..., x=..., y=....) 

Different elapsed hour per t.type / day

day t_type measure_hour
0   cd4    36
18  cd4    72

Merge above table with incucyte_dt

merge(incucyte_dt, measure_hour_table, on=...)[Elasped == measure_hour]

Data Quality Polishing / To Dos

• first hour(s) for some days/reps are weird, too low, it jumps up ** day 15, cd4, donor 3 and donor 4 esp. ** got rid of 1-2 hours at beginning of every replicate/donor?

ggplot(incucyte_dt[Elapsed < 10 & measurement == 'redintensity' & day_f == 8 & t_type == 'cd4' & k562 == 'mkate_cd19' & noise == F]) + geom_line(aes(y=mean_value_none, x=Elapsed, color = interaction(image, rep))) + facet_grid(donor+rep~car)

• inspect plots to remove outliers? For instance, one of the replicates (rep 2 donor 2, cd4, day 8, 41bb) looks like no t cells while the other looks fine.

ggplot(incucyte_dt[measurement == 'redintensity' & day_f == 8 & t_type == 'cd4' & k562 == 'mkate_cd19' & noise == F]) + geom_line(aes(y=value, x=Elapsed, color = interaction(image, rep))) + facet_grid(donor+rep~car)

Dan’s suggestion: take stdev of images within replicate - is one a huge outlier? then, take stdev of replicate means to mean of both replicates - how similar are they?

incucyte_new_dt <- copy(incucyte_dt)
NORM_HOUR_CUTOFF <- 2

incucyte_new_dt[, value_norm := NULL]
incucyte_new_dt[, mean_value_norm := NULL]
incucyte_new_dt[, std_dev := NULL]
incucyte_new_dt[, std_error := NULL]

incucyte_new_dt[, value_norm := value/(value[Elapsed==NORM_HOUR_CUTOFF]+0.001), 
  by=c('rep','day','donor','car','measurement','k562','image','t_type')]

incucyte_new_dt[, value_norm_img_diff := value_norm/mean(value_norm), 
  by=c('rep','day','donor','car','measurement','k562','t_type','Elapsed')][,
    value_norm_img_diff := mean(value_norm_img_diff[Elapsed < 150], na.rm=T), 
    c('rep','day','donor','car','measurement','k562','t_type','image')]

# plot of value norm differences for day 0, cd4
ggplot(incucyte_new_dt[measurement == 'redintensity' & 
    t_type == 'cd4' & day_f==0 & k562 == 'mkate_cd19' & noise == F]) + 
  geom_line(aes(y=value_norm_img_diff, x=Elapsed,
    color = interaction(image, rep))) + 
  facet_grid(donor+rep~car)

# mean difference from norm per day for cd4s
ggplot(incucyte_new_dt[measurement == 'redintensity' & t_type == 'cd4' & 
    k562 == 'mkate_cd19' & noise == F]) + 
  geom_point(aes(y=value_norm_img_diff, x=day_f, 
    color = interaction(image, rep))) + 
  facet_grid(donor+rep~car)

# mean difference from norm per day for cd8s
ggplot(incucyte_new_dt[measurement == 'redintensity' & t_type == 'cd8' & 
    k562 == 'mkate_cd19' & noise == F]) + 
  geom_point(aes(y=value_norm_img_diff, x=day_f,
    color = interaction(image, rep))) + 
  facet_grid(donor+rep~car)

Based on looking at this with Kendall, we decided to not remove bad images and take the mean of everything, but to remove bad replicates.

incucyte_new_dt[, mean_value_norm_rep := mean(value_norm),
  by=c('day','donor','car','Elapsed','measurement','k562','t_type','rep')]

incucyte_new_dt[, std_dev_norm_rep := sd(mean_value_norm_rep),
  by=c('day','donor','car','Elapsed','measurement','k562','t_type','rep')][,
    std_dev_norm_rep := mean(std_dev_norm_rep[Elapsed < 150], na.rm=T), 
    c('rep','day','donor','car','measurement','k562','t_type')]

#(make plots, compare, check, investigate bad replicate, like (rep 2 donor 2, cd4, day 8, 41bb))

# cd4 mean_value_norm_rep comparisons
ggplot(incucyte_new_dt[measurement=='redintensity' & t_type=='cd4' & 
                         k562=='mkate_cd19' & noise==F & !is.na(rep) & day_f==0]) +
  geom_line(aes(x=Elapsed, y=mean_value_norm_rep, color=as.factor(rep))) +
  facet_grid(donor~car)

# day 0: both reps look good for all cars
# day 8: donor 2 41BB (1 vs 3), donor 1 zeta (5 vs ~7), and donor 2 KLRG1 (6 vs 8)
# day 15: donor 3 zeta (5 vs 8)
# day 22: donor 1 BAFF-R lines look a little different but not bad? (1.25 vs 2.5)
# day 29: donor 2 zeta lines look a bit different (~1.3 vs ~2.7)
# also general note: D8/15/22 have 2 reps for donors 3 & 4 but D0 does not. true in csv.


# cd8 mean_value_norm_rep comparisons
ggplot(incucyte_new_dt[measurement=='redintensity' & t_type=='cd8' & 
                         k562=='mkate_cd19' & noise==F & !is.na(rep) & day_f==0]) +
  geom_line(aes(x=Elapsed, y=mean_value_norm_rep, color=as.factor(rep))) +
  facet_grid(donor~car)

# day 0: both reps look good for all cars
# day 8: donor 2 CD28 lines look a bit different (0 vs ~1)
# day 15: both reps look good for all cars
# day 22: donor 1 BAFF-R lines are a little different (~0.7 vs ~1.5)
# day 29: looks pretty good